CENG 3516 STATISTICAL COMPUTING

Introduction to ggplot2

ggplot2 has a slightly steeper learning curve than the base graphics functions, but it also generally produces far better and more easily customizable graphics.

There are two basic calls in ggplot:

• qplot(x, y, ..., data): a “quick-plot” routine, which essentially replaces the base plot()
• ggplot(data, aes(x, y, ...), ...): defines a graphics object from which plots can be generated, along with aesthetic mappings that specify how variables are mapped to visual properties.

library(ggplot2)

## Registered S3 methods overwritten by 'tibble':
##   method     from
##   format.tbl pillar
##   print.tbl  pillar

plot vs qplot

Here’s how the default scatterplots look in ggplot compared to the base graphics. We’ll illustrate things by continuing to use the birthwt data from the MASS library.

with(birthwt, plot(mother.age, birthwt.grams))  # Base graphics 

qplot(x=mother.age, y=birthwt.grams, data=birthwt)  # using qplot from ggplot2

Remember how it took us some effort last time to add color coding, use different plotting characters, and add a legend? Here’s the qplot call that does it all in one simple line.

qplot(x=mother.age, y=birthwt.grams, data=birthwt,
      color = mother.smokes,
      shape = mother.smokes,
      xlab = "Mother's age (years)",
      ylab = "Baby's birthweight (grams)"
      ) 

This way you won’t run into problems of accidentally producing the wrong legend. The legend is produced based on the colour and shape argument that you pass in. (Note: color and colour have the same effect. )

ggplot function

The ggplot2 library comes with a dataset called diamonds. Let’s look at it

dim(diamonds)

## [1] 53940    10

head(diamonds)

## Warning: `...` is not empty.
##
## We detected these problematic arguments:
## * `needs_dots`
##
## These dots only exist to allow future extensions and should be empty.
## Did you misspecify an argument?

## # A tibble: 6 x 10
##   carat cut       color clarity depth table price     x     y     z
##   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
## 2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
## 3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
## 4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
## 5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
## 6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48

It is a data frame of 53,940 diamonds, recording their attributes such as carat, cut, color, clarity, and price.

We will make a scatterplot showing the price as a function of the carat (size). (The data set is large so the plot may take a few moments to generate.)

diamond.plot <- ggplot(data=diamonds, aes(x=carat, y=price))
diamond.plot + geom_point()

The data set looks a little weird because a lot of diamonds are concentrated on the 1, 1.5 and 2 carat mark.

Let’s take a step back and try to understand the ggplot syntax.

1. The first thing we did was to define a graphics object, diamond.plot. This definition told R that we’re using the diamonds data, and that we want to display carat on the x-axis, and price on the y-axis.

2. We then called diamond.plot + geom_point() to get a scatterplot.

The arguments passed to aes() are called mappings. Mappings specify what variables are used for what purpose. When you use geom_point() in the second line, it pulls x, y, colour, size, etc., from the mappings specified in the ggplot() command.

You can also specify some arguments to geom_point directly if you want to specify them for each plot separately instead of pre-specifying a default.

Here we shrink the points to a smaller size, and use the alpha argument to make the points transparent.

diamond.plot + geom_point(size = 0.7, alpha = 0.3)

If we wanted to let point color depend on the color indicator of the diamond, we could do so in the following way.

diamond.plot <- ggplot(data=diamonds, aes(x=carat, y=price, colour = color))
diamond.plot + geom_point()

If we didn’t know anything about diamonds going in, this plot would indicate to us that D is likely the highest diamond grade, while J is the lowest grade.

We can change colors by specifying a different color palette. Here’s how we can switch to the cbPalette we saw last class.

cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
diamond.plot <- ggplot(data=diamonds, aes(x=carat, y=price, colour = color))
diamond.plot + geom_point() + scale_colour_manual(values=cbPalette)

To make the scatterplot look more typical, we can switch to logarithmic coordinate axis spacing.

diamond.plot + geom_point() +
  coord_trans(x = "log10", y = "log10")

Conditional plots

We can create plots showing the relationship between variables across different values of a factor. For instance, here’s a scatterplot showing how diamond price varies with carat size, conditioned on color. It’s created using the facet_wrap(~ factor1 + factor2 + ... + factorn) command.

diamond.plot <- ggplot(data=diamonds, aes(x=carat, y=price, colour = color))

diamond.plot + geom_point() + facet_wrap(~ cut)

You can also use facet_grid() to produce this type of output.

diamond.plot + geom_point() + facet_grid(. ~ cut)

diamond.plot + geom_point() + facet_grid(cut ~ .)

ggplot can create a lot of different kinds of plots, just like lattice. Here are some examples.

Function	Description
geom_point(...)	Points, i.e., scatterplot
geom_bar(...)	Bar chart
geom_line(...)	Line chart
geom_boxplot(...)	Boxplot
geom_violin(...)	Violin plot
geom_density(...)	Density plot with one variable
geom_density2d(...)	Density plot with two variables
geom_histogram(...)	Histogram

A bar chart

qplot(x = race, data = birthwt, geom = "bar")

Histograms and density plots

base.plot <- ggplot(birthwt, aes(x = mother.age)) +
  xlab("Mother's age")
base.plot + geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

base.plot + geom_histogram(aes(fill = race))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

base.plot + geom_density()

base.plot + geom_density(aes(fill = race), alpha = 0.5)

Box plots and violin plots

base.plot <- ggplot(birthwt, aes(x = as.factor(physician.visits), y = birthwt.grams)) +
  xlab("Number of first trimester physician visits") +
  ylab("Baby's birthweight (grams)")

# Box plot
base.plot + geom_boxplot()

# Violin plot
base.plot + geom_violin()

Visualizing means

Previously we calculated the following table:

bwt.summary <- aggregate(birthwt.grams ~ race + mother.smokes, data = birthwt, FUN = mean) # aggregate
bwt.summary

##    race mother.smokes birthwt.grams
## 1 black            no      2854.500
## 2 other            no      2815.782
## 3 white            no      3428.750
## 4 black           yes      2504.000
## 5 other           yes      2757.167
## 6 white           yes      2826.846

We can plot this table in a nice bar chart as follows:

# Define basic aesthetic parameters
p.bwt <- ggplot(data = bwt.summary, aes(y = birthwt.grams, x = race, fill = mother.smokes))

# Pick colors for the bars
bwt.colors <- c("#009E73", "#999999")

# Display barchart
p.bwt + geom_bar(stat = "identity", position = "dodge") +
  ylab("Average birthweight") +
  xlab("Mother's race") +
  guides(fill = guide_legend(title = "Mother's smoking status")) +
  scale_fill_manual(values=bwt.colors)

Does the association between birthweight and mother’s age depend on smoking status?

We previously ran the following command to calculate the correlation between mother’s ages and baby birthweights.

by(data = birthwt[c("birthwt.grams", "mother.age")],
   INDICES = birthwt["mother.smokes"],
   FUN = function(x) {cor(x[,1], x[,2])})

## mother.smokes: no
## [1] 0.2014558
## ------------------------------------------------------------
## mother.smokes: yes
## [1] -0.1441649

Here’s a visualization of our data that allows us to see what’s going on.

ggplot(birthwt, aes(x=mother.age, y=birthwt.grams, shape=mother.smokes, color=mother.smokes)) +
  geom_point() + # Adds points (scatterplot)
  geom_smooth(method = "lm") + # Adds regression lines
  ylab("Birth Weight (grams)") + # Changes y-axis label
  xlab("Mother's Age (years)") + # Changes x-axis label
  ggtitle("Birth Weight by Mother's Age") # Changes plot title

## `geom_smooth()` using formula 'y ~ x'